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Leukemia & Lymphoma Volume 58, 2017 - Issue 11
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Prognostic models in predicting outcomes in myelodysplastic syndromes after hypomethylating agent failure

Sagar S. PatelLeukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USAORCID Iconhttp://orcid.org/0000-0001-9949-3615
ORCID Icon,
Mikkael A. SekeresLeukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
&
Aziz NazhaLeukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USACorrespondence[email protected]
Pages 2532-2539 | Received 04 Jan 2017, Accepted 07 Mar 2017, Published online: 28 Mar 2017

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Haruka Teshigawara-Tanabe, Maki Hagihara, Jun Aoki, Satoshi Koyama, Hiroyuki Takahashi, Yuki Nakajima, Hiroyoshi Kunimoto, Takayoshi Tachibana, Takuya Miyazaki, Kenji Matsumoto, Masatsugu Tanaka, Etsuko Yamazaki, Shin Fujisawa, Heiwa Kanamori, Masataka Taguri & Hideaki Nakajima. (2022) Clinical risk factors for patients with myelodysplastic syndromes undergoing allogeneic hematopoietic stem cell transplantation. Hematology 27:1, pages 620-628.
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Jan Philipp Bewersdorf & Amer M. Zeidan. (2020) Management of higher risk myelodysplastic syndromes after hypomethylating agents failure: are we about to exit the black hole?. Expert Review of Hematology 13:10, pages 1131-1142.
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Articles from other publishers (2)

Nathan Radakovich, Matthew Nagy & Aziz Nazha. (2020) Machine learning in haematological malignancies. The Lancet Haematology 7:7, pages e541-e550.
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Nathan Radakovich, Matthew Nagy & Aziz Nazha. (2020) Artificial Intelligence in Hematology: Current Challenges and Opportunities. Current Hematologic Malignancy Reports 15:3, pages 203-210.
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